{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20],\n",
       "       [52],\n",
       "       [42]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "a = [[1,2,3,4],[5,6,7,8],[2,3,7,9]]\n",
    "b = [[2],[2],[2],[2]]\n",
    "np.dot(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11e467990>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([60,72,75,80,83],[126,151.2,157.5,168,174.3])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
